Volume 54 - Article 23 | Pages 719–762  

Probabilistic population forecasts for small regions

By Julius Goes, Henriette Engelhardt

Abstract

Background: Age-specific population forecasts for small areas or subnational regions are a valuable tool for local governments. However, typical population projection methods based on the cohort-component approach are difficult to apply on a smaller subnational scale.

Objective: We introduce Bayesian methods suitable for obtaining reliable age-specific population forecasts for small regions using the cohort-component method.

Methods: Our approach improves fertility forecasting by extending the Lee–Carter model with an age-region interaction term. We propose to forecast net-migration counts using skewed error terms, and introduce a Dirichlet regression to model migration age patterns as well as age proportions of fertility.

Results: We run our model to produce age-specific population forecasts for a set of 13 heterogeneous regions in Bavaria, Germany. We compare our method with other standard approaches and find that it produces superior out-of-sample forecasts according to both point measures and scoring rules.

Conclusions: The findings suggest that the proposed Bayesian methods offer good predictive accuracy and are suitable in obtaining precise forecasts of age-specific population for smaller geo-graphical regions.

Contribution: We introduce a new method for the probabilistic projection of subnational population that works well and outperforms other current methods.

Author’s Affiliation

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On the age-specific correlation between fertility and female employment: Heterogeneity over space and time in OECD countries
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